Using a patient-centred composite endpoint in a secondary analysis of the Control of Hypertension in Pregnancy Study (CHIPS) Trial
Bibliographic record
Abstract
BACKGROUND: Clinical trials commonly use multiple endpoints to measure the impact of an intervention. While this improves the comprehensiveness of outcomes, it can make trial results difficult to interpret. We examined the impact of integrating patient weights into a composite endpoint on the interpretation of Control of Hypertension in Pregnancy Study (CHIPS) Trial results. METHODS: Outcome weights were extracted from a previous patient preferences study in pregnancy hypertension (N = 183 women) which identified (i) seven outcomes most important to women (taking medication, severe hypertension, pre-eclampsia, blood transfusion, Caesarean, delivery < 34 weeks, and baby born smaller-than-expected) and (ii) three preference subgroups: (1) 'equal prioritizers', 62%; (2) 'early delivery avoiders', 23%; and (3) 'medication minimizers', 14%. Outcome weights from the preference subgroups were integrated with CHIPS data for the seven outcomes identified in the preference study. A weighted composite score was derived for each participant by multiplying the preference weight for each outcome by the binary outcome if it occurred. Analyses considered equal weights and those from the preference subgroups. The mean composite scores were compared between trial arms (t-tests). RESULTS: Composite scores were similar between trial arms with the use of equal weights or those of subgroup (1) (95% confidence intervals [CIs]: - 0.03, 0.02; p > 0.50 for each). 'Tight' control was superior when using subgroup (2) weights (95% CIs: 0.002, 0.07; p = 0.03), and 'less-tight' control was superior when using subgroup (3) weights (95% CIs: - 0.11, - 0.04; p < 0.01). CONCLUSIONS: Evidence-based recommendations for 'tight' control are consistent with most women's preferences, but for a sixth of women, 'less-tight' control is more preference consistent. Depending on patient preferences, a single trial may support different interventions. Future trials should specify component weights to improve interpretation. TRIAL REGISTRATION: ClinicalTrials.gov NCT01192412.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.078 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".